Hello Reader,

"How are you using GenAI in your day-to-day work?" This question shows up in almost every interview now, and almost everyone gives the same answer.

• "I use GenAI to write code."
• "I use it to generate documents."
• "I have a bot that automates part of my job."

These are bad answers. Not because they are false, but because they tell the interviewer nothing about you specifically.

The prompt is not the differentiator. Anyone can type a prompt into Claude or ChatGPT and get code back. What the interviewer is actually trying to find out is whether you know how to use your own judgment on top of the output, because at the end of the day, every one of these models is nondeterministic.

The same prompt can generate a different result on two separate runs. The candidate who blindly trusts the output looks identical to someone who has never touched the tool. The candidate who catches what the model got wrong looks like someone who actually understands the system underneath it.

Here are concrete examples you can adapt and use in your own interviews.

Unnecessary validation logic

GenAI tends to over-engineer. If you accept a field from the front end and just need to insert it into a database, the model will often add validation checks that duplicate what already exists on the front end. Left unchecked, this bloats your codebase and makes it harder to maintain.

Catching and removing that redundant logic, and being able to explain why you removed it, shows you are thinking about long-term maintainability, not just shipping something that runs.

Infrastructure as code that is not secure by default

If you are a Solutions Architect, DevOps engineer, or cloud engineer, this example lands well. GenAI-generated CloudFormation or Terraform is inconsistent. One run might generate a security group open to the entire internet. One time it might generate in YAML, next run in JSON.

The fix is not to catch this manually every time. It is to create a skill, a reusable instruction set, that tells the model: always generate CloudFormation in YAML, never JSON, never leave a security group open to the internet, and require additional approval for any internet-facing load balancer.

Mentioning "skills" and "nondeterministic" by name in an interview signals that you are current with the latest patterns, not just the basics.

DynamoDB and the missing global secondary index

Going little deeper on this one. If you have built serverless applications, this is a strong one. "I was recently coding a serverless microservice using API Gateway, Lambda, and DynamoDB. My tables had both a primary index and a global secondary index. When I asked GenAI to add a new feature, it generated a DynamoDB scan instead of a query. A scan reads the entire table with no index, which is inefficient and expensive at scale. I reviewed the generated code, caught that it ignored the GSI, and told it explicitly which fields to query on instead."

That answer shows you know how to review AI-generated code for performance, not just correctness.

Slide generation and system diagrams that are subtly wrong

GenAI can produce a full slide deck or architecture diagram almost instantly, but it makes small factual errors that are easy to miss if you are not paying attention.

A common one: claiming spot instances save up to 75 percent, when that number actually applies to reserved instances. Spot instance savings are typically over 90 percent.

Another common one on system diagrams: showing SQS invoking SNS, when the correct direction is SNS publishing to SQS, since SNS is a one-to-many pub/sub service and SQS is a queue that receives from it, not the other way around.

Catching these small inversions and misattributions is a strong signal that you understand the actual services, not just what the tool generated about them.

Requesting verified sources, not just an answer

A more advanced technique: when generating a system design with GenAI, explicitly ask for official AWS documentation links for every service and every step, and instruct the model not to include unofficial sources. There is enough noise online now that models will sometimes pull from blogs or third-party content that is outdated or incorrect. Requiring official documentation as a validation step is a habit that shows real production discipline.

One more habit that consistently improves GenAI output

When you are working through a multi-step task, list every requirement up front instead of feeding instructions one at a time. If you need a microservice enhanced to add a discount feature, tell the model up front to reuse the existing DynamoDB tables, use the existing primary and secondary indexes, query instead of scan, reference the specific CloudFormation file for the GSI list, and check for any other APIs the change might impact.

Giving the full picture at once produces dramatically better output than correcting the model step by step, and it also reduces token usage and cost, since re-explaining context repeatedly burns more tokens each time.

The pattern underneath every one of these examples

None of these answers are longer for the sake of being longer. Every one of them follows the same structure: name a specific real-world scenario, explain what GenAI got wrong or generated inefficiently, and explain how your own judgment corrected it. That structure is what makes the difference between sounding like a user of AI tools and sounding like someone who directs them.

The next time someone asks how you are using GenAI, do not say "I use Claude to code" or "I use a bot to automate my work." Pick two or three of these patterns, adapt them to your own experience, and give an answer that actually demonstrates judgment. That is the answer that gets remembered after the interview ends.

Keep learning and keep rocking 🚀,

Raj

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